Comparison between different deep neural network and generative Adversarial Networks

In this, we provide an overview of the three major DNN approaches related to various application of image processing, that are Autoencoders, Convolutional Neural Networks and Generative Adversarial Networks.

Convolutional Neural Networks

Convolutional Neural Networks are deep neural networks which involve the use of convolutions operation instead of the more traditional matrix multiplications, in several layers, and are used for process regularly sampled data such as 2D and 3D imagery. Convolutional Neural Networks architectures for image analysis tasks are composed of four key layers that are convolutional layers, nonlinear activations, pooling and fully connected layers.

Autoencoders

A autoencoder is a deterministic deep neural network contain input and an output layer of the same size with a hidden layer in between, which is trained based on different algorithm like back propagation algorithm aiming to learn features of the input in which encoder encodes the input and decoder decodes the extracted features and after that image is get reconstructed.

Generative Adversarial Networks

Generative Adversarial Networks new approach in the deep neural network has led to a significant increase in terms of computational performance like accuracy, elimination of redundant data and quality of image, as well as generate new types of learning algorithm, such as generating realistic images. In Generative Adversarial Networks training involves two deep neural networks, generator and discriminator network, where the generator first synthesizes various signals given as input and later it classifies inputs as real or fake. For image enhancement problems, a variant of Generative Adversarial Networks, called conditional Generative Adversarial Networks, is more important since the input to the generator is the image itself, and based on the condition the new image gets generated which is similar to input images.

Comparison between different deep neural network and generative adversarial networks

It may be useful to compare generative adversarial networks to other neural networks, such as autoencoders and convolutional neural networks. Autoencoders encode input data and create a

hidden representation of the data and they are useful in dimensionality reduction, that is data serving as a hidden representation compresses the data into a smaller number of dimensions and then it paired with a decoder, which allows you to reconstruct input data based on its hidden data. Autoencoders are generative algorithms that synthesize data like generative adversarial networks. However, while generative adversarial networks generate data in fine, realistic and granular detail,images generated by Autoencoders tend to be more blurred and in terms of convolutional neural networks, convolutional neural networks are a deep neural network which produces results on computer vision tasks such as object detection and faces recognition in which it uses different architecture to produce different results based on inputs and when it compares to generative adversarial networks which contain two models same as convolutional neural networks which produce better accuracy and details between images as that generative adversarial networks are preferred against various types of deep neural network.